CBTI Summary

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Data analysis

Table

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isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st , is at 13.53 (95% CI [12.97, 14.09], t(846) = 47.48, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(846) = -0.32, p = 0.750; Std. beta = -0.03, 95% CI [-0.21, 0.15])
  • The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.83], t(846) = -7.67, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
  • The effect of time point [3rd] is statistically significant and negative (beta = -2.84, 95% CI [-3.49, -2.19], t(846) = -8.57, p < .001; Std. beta = -0.64, 95% CI [-0.78, -0.49])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.91, -2.01], t(846) = -6.13, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.99, 95% CI [-3.96, -2.02], t(846) = -6.05, p < .001; Std. beta = -0.67, 95% CI [-0.88, -0.45])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st , is at 9.82 (95% CI [9.22, 10.42], t(846) = 32.18, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.68, 1.01], t(846) = 0.38, p = 0.707; Std. beta = 0.04, 95% CI [-0.16, 0.24])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.73, 95% CI [0.14, 1.32], t(846) = 2.43, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
  • The effect of time point [3rd] is statistically significant and positive (beta = 0.94, 95% CI [0.33, 1.54], t(846) = 3.01, p = 0.003; Std. beta = 0.22, 95% CI [0.08, 0.37])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.40, 95% CI [0.51, 2.29], t(846) = 3.09, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 1.62, 95% CI [0.71, 2.53], t(846) = 3.49, p < .001; Std. beta = 0.38, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st , is at 8.21 (95% CI [7.47, 8.95], t(846) = 21.71, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(846) = 1.11, p = 0.268; Std. beta = 0.11, 95% CI [-0.09, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.12], t(846) = -2.33, p = 0.020; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.63, 95% CI [-1.30, 0.05], t(846) = -1.81, p = 0.070; Std. beta = -0.12, 95% CI [-0.25, 0.01])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(846) = -3.42, p < .001; Std. beta = -0.33, 95% CI [-0.53, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.46, 95% CI [-3.48, -1.44], t(846) = -4.74, p < .001; Std. beta = -0.47, 95% CI [-0.67, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st , is at 7.54 (95% CI [6.79, 8.28], t(846) = 19.77, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(846) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(846) = -1.29, p = 0.197; Std. beta = -0.08, 95% CI [-0.21, 0.04])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.63, 95% CI [-1.32, 0.07], t(846) = -1.78, p = 0.076; Std. beta = -0.12, 95% CI [-0.26, 0.01])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(846) = -4.00, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.33, 95% CI [-3.37, -1.29], t(846) = -4.40, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st , is at 16.77 (95% CI [15.30, 18.24], t(846) = 22.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(846) = -0.08, p = 0.937; Std. beta = -8.25e-03, 95% CI [-0.21, 0.20])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.55], t(846) = -1.18, p = 0.239; Std. beta = -0.08, 95% CI [-0.22, 0.05])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.57, 1.26], t(846) = -0.21, p = 0.834; Std. beta = -0.01, 95% CI [-0.15, 0.12])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.88], t(846) = -2.79, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.88, 95% CI [-7.00, -2.76], t(846) = -4.51, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.02 (95% CI [2.91, 3.13], t(846) = 54.50, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.94e-03, 0.32], t(846) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(846) = -3.29, p < .001; Std. beta = -0.25, 95% CI [-0.40, -0.10])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(846) = -3.61, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(846) = -5.32, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.38], t(846) = -6.11, p < .001; Std. beta = -0.71, 95% CI [-0.94, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.53 (95% CI [3.40, 3.66], t(846) = 53.05, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(846) = 0.44, p = 0.660; Std. beta = 0.05, 95% CI [-0.16, 0.25])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(846) = -1.68, p = 0.092; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.24, 2.45e-03], t(846) = -1.92, p = 0.055; Std. beta = -0.13, 95% CI [-0.26, 2.67e-03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(846) = -3.80, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.44, 95% CI [-0.62, -0.26], t(846) = -4.71, p < .001; Std. beta = -0.48, 95% CI [-0.68, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.99 (95% CI [1.89, 2.08], t(846) = 38.96, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-9.01e-03, 0.27], t(846) = 1.83, p = 0.067; Std. beta = 0.19, 95% CI [-0.01, 0.40])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(846) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.01, 95% CI [-0.09, 0.12], t(846) = 0.28, p = 0.780; Std. beta = 0.02, 95% CI [-0.13, 0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(846) = -3.18, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.49, -0.18], t(846) = -4.31, p < .001; Std. beta = -0.49, 95% CI [-0.71, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 2.33 (95% CI [2.21, 2.45], t(846) = 38.40, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(846) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(846) = -0.97, p = 0.331; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.17, 0.07], t(846) = -0.81, p = 0.416; Std. beta = -0.06, 95% CI [-0.21, 0.09])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(846) = -0.94, p = 0.346; Std. beta = -0.10, 95% CI [-0.32, 0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.45, -0.09], t(846) = -2.89, p = 0.004; Std. beta = -0.33, 95% CI [-0.55, -0.11])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st , is at 6.59 (95% CI [6.31, 6.86], t(846) = 46.89, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(846) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(846) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.68, 95% CI [-0.96, -0.39], t(846) = -4.62, p < .001; Std. beta = -0.34, 95% CI [-0.48, -0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(846) = -5.21, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.29, 95% CI [-1.72, -0.87], t(846) = -5.92, p < .001; Std. beta = -0.64, 95% CI [-0.85, -0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st , is at 14.20 (95% CI [13.65, 14.76], t(846) = 50.07, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.45, 1.13], t(846) = 0.85, p = 0.396; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.87, -0.60], t(846) = -3.80, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.86, 95% CI [-2.52, -1.21], t(846) = -5.56, p < .001; Std. beta = -0.45, 95% CI [-0.61, -0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.67, -1.76], t(846) = -5.56, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.85, 95% CI [-3.83, -1.87], t(846) = -5.71, p < .001; Std. beta = -0.69, 95% CI [-0.92, -0.45])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st , is at 7.17 (95% CI [6.84, 7.51], t(846) = 41.59, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(846) = -1.17, p = 0.243; Std. beta = -0.12, 95% CI [-0.31, 0.08])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 2.04e-03], t(846) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 8.34e-04])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.76, 95% CI [-1.12, -0.40], t(846) = -4.18, p < .001; Std. beta = -0.31, 95% CI [-0.46, -0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(846) = -4.69, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.29, 95% CI [-1.83, -0.76], t(846) = -4.74, p < .001; Std. beta = -0.53, 95% CI [-0.75, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.15 (95% CI [2.83, 3.46], t(846) = 19.57, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(846) = 0.39, p = 0.694; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(846) = 2.22, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.05, 0.62], t(846) = 1.68, p = 0.093; Std. beta = 0.13, 95% CI [-0.02, 0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(846) = -2.67, p = 0.008; Std. beta = -0.31, 95% CI [-0.53, -0.08])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.84, 95% CI [-1.34, -0.34], t(846) = -3.29, p = 0.001; Std. beta = -0.39, 95% CI [-0.62, -0.16])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.86 (95% CI [1.76, 1.96], t(846) = 36.72, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(846) = 0.62, p = 0.533; Std. beta = 0.07, 95% CI [-0.14, 0.27])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(846) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 7.90e-03, 95% CI [-0.09, 0.10], t(846) = 0.16, p = 0.872; Std. beta = 0.01, 95% CI [-0.13, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(846) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.38, -0.10], t(846) = -3.26, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st , is at 2.87 (95% CI [2.75, 3.00], t(846) = 45.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.28], t(846) = 1.10, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(846) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.36, 95% CI [-0.49, -0.23], t(846) = -5.48, p < .001; Std. beta = -0.41, 95% CI [-0.55, -0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(846) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.27])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.41, 95% CI [-0.60, -0.21], t(846) = -4.10, p < .001; Std. beta = -0.46, 95% CI [-0.68, -0.24])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st , is at 10.72 (95% CI [10.26, 11.19], t(846) = 45.23, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.37, 0.95], t(846) = 0.87, p = 0.386; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.31, 95% CI [-1.82, -0.81], t(846) = -5.08, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.32, 95% CI [-1.85, -0.80], t(846) = -4.94, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.86, 95% CI [-2.63, -1.10], t(846) = -4.77, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.22, -1.65], t(846) = -6.11, p < .001; Std. beta = -0.71, 95% CI [-0.93, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.30 (95% CI [1.19, 1.41], t(846) = 22.94, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(846) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(846) = -0.39, p = 0.695; Std. beta = -0.03, 95% CI [-0.17, 0.11])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(846) = 0.50, p = 0.619; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(846) = -2.94, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.21], t(846) = -4.44, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.28 (95% CI [1.17, 1.39], t(846) = 22.04, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(846) = 0.82, p = 0.414; Std. beta = 0.09, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(846) = -0.62, p = 0.537; Std. beta = -0.04, 95% CI [-0.18, 0.09])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.17, 0.05], t(846) = -1.05, p = 0.293; Std. beta = -0.08, 95% CI [-0.22, 0.06])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.29e-03], t(846) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 2.90e-03])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.43, -0.10], t(846) = -3.09, p = 0.002; Std. beta = -0.33, 95% CI [-0.54, -0.12])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.33 (95% CI [1.22, 1.44], t(846) = 23.33, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(846) = 0.81, p = 0.416; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(846) = 0.61, p = 0.539; Std. beta = 0.04, 95% CI [-0.09, 0.17])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.07, 95% CI [-0.17, 0.03], t(846) = -1.32, p = 0.188; Std. beta = -0.09, 95% CI [-0.22, 0.04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(846) = -3.55, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.21, 95% CI [-0.37, -0.06], t(846) = -2.68, p = 0.007; Std. beta = -0.27, 95% CI [-0.48, -0.07])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 46.33 (95% CI [45.04, 47.63], t(846) = 70.32, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.97, 1.69], t(846) = -0.15, p = 0.882; Std. beta = -0.02, 95% CI [-0.22, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.29], t(846) = -1.47, p = 0.141; Std. beta = -0.10, 95% CI [-0.23, 0.03])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.79, 95% CI [-1.99, 0.41], t(846) = -1.29, p = 0.197; Std. beta = -0.09, 95% CI [-0.22, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.52], t(846) = 3.07, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.21, 95% CI [1.41, 5.01], t(846) = 3.49, p < .001; Std. beta = 0.36, 95% CI [0.16, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st , is at 39.90 (95% CI [38.39, 41.40], t(846) = 51.88, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(846) = 0.08, p = 0.937; Std. beta = 8.00e-03, 95% CI [-0.19, 0.21])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.55, 3.45], t(846) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
  • The effect of time point [3rd] is statistically significant and positive (beta = 2.26, 95% CI [0.76, 3.76], t(846) = 2.95, p = 0.003; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.37, 5.77], t(846) = 3.18, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.67, 95% CI [2.42, 6.92], t(846) = 4.06, p < .001; Std. beta = 0.44, 95% CI [0.23, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

Post hoc analysis

Table

Between group

isi

1st

t(635.99) = -0.32, p = 0.750, Cohen d = 0.05, 95% CI (-0.92 to 0.66)

2st

t(752.55) = -6.62, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(772.57) = -6.52, p = 0.000, Cohen d = 1.10, 95% CI (-4.06 to -2.18)

who

1st

t(548.72) = 0.38, p = 0.708, Cohen d = -0.06, 95% CI (-0.69 to 1.01)

2st

t(687.01) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (0.60 to 2.52)

3rd

t(711.17) = 3.58, p = 0.000, Cohen d = -0.68, 95% CI (0.81 to 2.76)

phq

1st

t(501.03) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(636.46) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(660.50) = -3.08, p = 0.002, Cohen d = 0.64, 95% CI (-3.05 to -0.68)

gad

1st

t(506.74) = 0.89, p = 0.373, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(643.26) = -2.65, p = 0.008, Cohen d = 0.53, 95% CI (-2.77 to -0.41)

3rd

t(667.45) = -3.03, p = 0.003, Cohen d = 0.62, 95% CI (-3.05 to -0.65)

wsas

1st

t(523.23) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.69) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(686.09) = -4.11, p = 0.000, Cohen d = 0.82, 95% CI (-7.34 to -2.59)

shps_arousal

1st

t(601.26) = 2.07, p = 0.039, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(729.66) = -3.50, p = 0.000, Cohen d = 0.60, 95% CI (-0.49 to -0.14)

3rd

t(751.78) = -4.33, p = 0.000, Cohen d = 0.76, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(507.81) = 0.44, p = 0.660, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(644.52) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(668.73) = -3.71, p = 0.000, Cohen d = 0.76, 95% CI (-0.61 to -0.19)

shps_behavior

1st

t(555.48) = 1.83, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(693.16) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(717.16) = -2.47, p = 0.014, Cohen d = 0.46, 95% CI (-0.37 to -0.04)

shps_environment

1st

t(550.28) = -0.72, p = 0.470, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(688.45) = -1.52, p = 0.130, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(712.57) = -3.33, p = 0.001, Cohen d = 0.63, 95% CI (-0.53 to -0.14)

dbas_consequence

1st

t(560.41) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(697.51) = -4.68, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(721.37) = -5.38, p = 0.000, Cohen d = 1.00, 95% CI (-1.69 to -0.79)

dbas_worry

1st

t(648.13) = 0.85, p = 0.396, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(759.78) = -5.09, p = 0.000, Cohen d = 0.83, 95% CI (-3.29 to -1.46)

3rd

t(778.95) = -5.25, p = 0.000, Cohen d = 0.88, 95% CI (-3.45 to -1.57)

dbas_expectation

1st

t(570.53) = -1.17, p = 0.243, Cohen d = 0.18, 95% CI (-0.76 to 0.19)

2st

t(706.09) = -5.52, p = 0.000, Cohen d = 0.99, 95% CI (-2.08 to -0.99)

3rd

t(729.61) = -5.56, p = 0.000, Cohen d = 1.02, 95% CI (-2.14 to -1.02)

dbas_medication

1st

t(572.26) = 0.39, p = 0.694, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.52) = -2.22, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(730.98) = -2.83, p = 0.005, Cohen d = 0.52, 95% CI (-1.27 to -0.23)

psas_somatic

1st

t(524.51) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(663.05) = -3.25, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(687.44) = -2.38, p = 0.018, Cohen d = 0.47, 95% CI (-0.36 to -0.03)

psas_cognitive

1st

t(562.69) = 1.10, p = 0.270, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(699.47) = -3.28, p = 0.001, Cohen d = 0.60, 95% CI (-0.54 to -0.13)

3rd

t(723.27) = -2.95, p = 0.003, Cohen d = 0.55, 95% CI (-0.51 to -0.10)

psqi_global

1st

t(612.99) = 0.87, p = 0.386, Cohen d = -0.13, 95% CI (-0.37 to 0.95)

2st

t(737.79) = -4.07, p = 0.000, Cohen d = 0.69, 95% CI (-2.33 to -0.81)

3rd

t(759.26) = -5.43, p = 0.000, Cohen d = 0.94, 95% CI (-2.92 to -1.37)

mic_attention

1st

t(549.18) = 1.52, p = 0.129, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(687.43) = -1.39, p = 0.164, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(711.58) = -2.82, p = 0.005, Cohen d = 0.54, 95% CI (-0.44 to -0.08)

mic_executive

1st

t(526.14) = 0.82, p = 0.414, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(664.76) = -1.00, p = 0.317, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(689.17) = -2.07, p = 0.039, Cohen d = 0.41, 95% CI (-0.38 to -0.01)

mic_memory

1st

t(507.28) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.90) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(668.10) = -1.61, p = 0.108, Cohen d = 0.33, 95% CI (-0.33 to 0.03)

nb_pcs

1st

t(508.13) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(644.88) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(669.11) = 2.90, p = 0.004, Cohen d = -0.59, 95% CI (0.99 to 5.15)

nb_mcs

1st

t(538.91) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.05 to 2.22)

2st

t(677.68) = 2.98, p = 0.003, Cohen d = -0.56, 95% CI (1.25 to 6.06)

3rd

t(702.00) = 3.80, p = 0.000, Cohen d = -0.73, 95% CI (2.30 to 7.21)

Within treatment group

isi

1st vs 2st

t(591.59) = -14.99, p = 0.000, Cohen d = 1.92, 95% CI (-6.13 to -4.71)

1st vs 3rd

t(593.22) = -15.91, p = 0.000, Cohen d = 2.07, 95% CI (-6.55 to -5.11)

who

1st vs 2st

t(569.47) = 6.25, p = 0.000, Cohen d = -0.81, 95% CI (1.46 to 2.80)

1st vs 3rd

t(570.21) = 7.40, p = 0.000, Cohen d = -0.97, 95% CI (1.88 to 3.24)

phq

1st vs 2st

t(554.90) = -6.59, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(555.29) = -7.97, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.32)

gad

1st vs 2st

t(556.76) = -6.44, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(557.18) = -7.49, p = 0.000, Cohen d = 0.99, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(561.95) = -4.74, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.21)

1st vs 3rd

t(562.49) = -6.24, p = 0.000, Cohen d = 0.83, 95% CI (-6.62 to -3.45)

shps_arousal

1st vs 2st

t(583.37) = -10.00, p = 0.000, Cohen d = 1.29, 95% CI (-0.81 to -0.54)

1st vs 3rd

t(584.61) = -11.49, p = 0.000, Cohen d = 1.50, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(557.11) = -6.52, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(557.54) = -8.04, p = 0.000, Cohen d = 1.07, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(571.37) = -3.81, p = 0.000, Cohen d = 0.49, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(572.17) = -5.54, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(569.91) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(570.67) = -4.62, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas_consequence

1st vs 2st

t(572.74) = -9.03, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(573.58) = -12.12, p = 0.000, Cohen d = 1.59, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(594.31) = -10.80, p = 0.000, Cohen d = 1.38, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(596.09) = -12.73, p = 0.000, Cohen d = 1.65, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(575.48) = -7.96, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(576.42) = -10.14, p = 0.000, Cohen d = 1.33, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(575.94) = -1.59, p = 0.223, Cohen d = 0.21, 95% CI (-0.67 to 0.07)

1st vs 3rd

t(576.90) = -2.91, p = 0.007, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas_somatic

1st vs 2st

t(562.34) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(562.89) = -4.24, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(573.36) = -8.78, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(574.23) = -10.45, p = 0.000, Cohen d = 1.37, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(586.22) = -10.86, p = 0.000, Cohen d = 1.40, 95% CI (-3.75 to -2.60)

1st vs 3rd

t(587.59) = -12.69, p = 0.000, Cohen d = 1.65, 95% CI (-4.34 to -3.18)

mic_attention

1st vs 2st

t(569.60) = -4.26, p = 0.000, Cohen d = 0.55, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(570.35) = -5.51, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(562.84) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(563.40) = -5.09, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(556.94) = -4.18, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(557.37) = -4.77, p = 0.000, Cohen d = 0.63, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(557.21) = 2.79, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.22)

1st vs 3rd

t(557.64) = 3.52, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(566.65) = 6.61, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(567.31) = 8.10, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(539.07) = -7.67, p = 0.000, Cohen d = 0.87, 95% CI (-3.09 to -1.83)

1st vs 3rd

t(546.37) = -8.57, p = 0.000, Cohen d = 1.01, 95% CI (-3.49 to -2.19)

who

1st vs 2st

t(527.56) = 2.43, p = 0.031, Cohen d = -0.28, 95% CI (0.14 to 1.32)

1st vs 3rd

t(532.18) = 3.01, p = 0.005, Cohen d = -0.36, 95% CI (0.33 to 1.55)

phq

1st vs 2st

t(520.50) = -2.33, p = 0.040, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(523.80) = -1.81, p = 0.142, Cohen d = 0.21, 95% CI (-1.31 to 0.05)

gad

1st vs 2st

t(521.39) = -1.29, p = 0.396, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(524.84) = -1.78, p = 0.153, Cohen d = 0.21, 95% CI (-1.32 to 0.07)

wsas

1st vs 2st

t(523.88) = -1.18, p = 0.480, Cohen d = 0.13, 95% CI (-2.19 to 0.55)

1st vs 3rd

t(527.78) = -0.21, p = 1.000, Cohen d = 0.02, 95% CI (-1.57 to 1.27)

shps_arousal

1st vs 2st

t(534.65) = -3.29, p = 0.002, Cohen d = 0.37, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(540.83) = -3.61, p = 0.001, Cohen d = 0.43, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(521.55) = -1.68, p = 0.186, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(525.03) = -1.92, p = 0.111, Cohen d = 0.23, 95% CI (-0.24 to 0.00)

shps_behavior

1st vs 2st

t(528.51) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.12)

1st vs 3rd

t(533.32) = 0.28, p = 1.000, Cohen d = -0.03, 95% CI (-0.09 to 0.12)

shps_environment

1st vs 2st

t(527.78) = -0.97, p = 0.663, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(532.44) = -0.81, p = 0.834, Cohen d = 0.10, 95% CI (-0.17 to 0.07)

dbas_consequence

1st vs 2st

t(529.19) = -2.39, p = 0.035, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(534.15) = -4.62, p = 0.000, Cohen d = 0.55, 95% CI (-0.96 to -0.39)

dbas_worry

1st vs 2st

t(540.59) = -3.80, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.59)

1st vs 3rd

t(548.29) = -5.56, p = 0.000, Cohen d = 0.65, 95% CI (-2.52 to -1.20)

dbas_expectation

1st vs 2st

t(530.58) = -1.95, p = 0.104, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(535.83) = -4.17, p = 0.000, Cohen d = 0.49, 95% CI (-1.12 to -0.40)

dbas_medication

1st vs 2st

t(530.81) = 2.22, p = 0.053, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(536.11) = 1.68, p = 0.187, Cohen d = -0.20, 95% CI (-0.05 to 0.62)

psas_somatic

1st vs 2st

t(524.07) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(528.00) = 0.16, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.10)

psas_cognitive

1st vs 2st

t(529.51) = -3.20, p = 0.003, Cohen d = 0.36, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(534.53) = -5.48, p = 0.000, Cohen d = 0.65, 95% CI (-0.49 to -0.23)

psqi_global

1st vs 2st

t(536.16) = -5.08, p = 0.000, Cohen d = 0.58, 95% CI (-1.82 to -0.81)

1st vs 3rd

t(542.71) = -4.94, p = 0.000, Cohen d = 0.58, 95% CI (-1.85 to -0.80)

mic_attention

1st vs 2st

t(527.63) = -0.39, p = 1.000, Cohen d = 0.04, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(532.26) = 0.50, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.14)

mic_executive

1st vs 2st

t(524.31) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(528.29) = -1.05, p = 0.587, Cohen d = 0.12, 95% CI (-0.17 to 0.05)

mic_memory

1st vs 2st

t(521.47) = 0.61, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(524.94) = -1.31, p = 0.378, Cohen d = 0.16, 95% CI (-0.17 to 0.03)

nb_pcs

1st vs 2st

t(521.60) = -1.47, p = 0.283, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(525.09) = -1.29, p = 0.395, Cohen d = 0.15, 95% CI (-2.00 to 0.41)

nb_mcs

1st vs 2st

t(526.17) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.46)

1st vs 3rd

t(530.50) = 2.95, p = 0.007, Cohen d = -0.35, 95% CI (0.75 to 3.77)

Plot

Clinical significance